Rowland's interest within robotics lie at the intersection of control theory, machine learning, and optimization. Rowland came to NVIDIA from the Silicon Valley follow-me drone startup world developing algorithms for planning and control, as well as improving the dynamical models of the robotic vehicles to a high degree of fidelity. Before joining the startup world, Rowland earned a B.S. and M.S degree in ECE from UCSB and a Ph.D. in Robotics from Georgia Tech. Rowland’s dissertation, entitled “A Control Theoretic Perspective On Learning In Robotics”, identifies when a dynamical system is able to learn and how to optimally switch between exploration and exploitation by using ergodicity and effort as a metric.
At NVIDIA Rowland brings his robotics research background and software engineering experience to the Seattle Robotics Lab as a Senior Robotics Research Software Engineer. His work involves research in planning and optimal control for robotic systems as well as managing the software development process in the lab. From the software engineering side, the goal is to take research developed in the lab from research code to a professional software product that can be used across the company or the world to advance robotics while leveraging the NVIDIA GPU.